DrugCoding: A Scientific Programming Agent for Drug Discovery

Key points

DrugCoding is a drug discovery agent system that unifies scientific task planning, adaptive model routing, multi-agent collaboration, and programmatic tool execution.
It decomposes research objectives into dependency graphs and selects specialized models for each task by applicability, performance, and cost.
On the multi-turn M-TCDD benchmark, it achieved an average Pass@1 of 97.35% across seven language models, ahead of the strongest baseline Codex at 88.11% by 9.24%.
On the top 10% challenging subset, it averaged 97.92%, surpassing scientific research agents.
Failure analysis showed that accurate preservation of scientific inputs was the main source of its advantage.
The multi-agent configuration improved over the single-agent version by an average of only 0.56%, varying widely across models.

External research summaries. These are not HDATF publications or measured product results.